syedmokthiyar / project

project

Aim

To write a python program using OpenCV to do the following image manipulations. i) Extract ROI from an image. ii) Perform handwritting detection in an image. iii) Perform object detection with label in an image.

Software Required:

Anaconda - Python 3.7

Algorithm:

I)Perform ROI from an image

Step1:

Import necessary packages

Step2:

Read the image and convert the image into RGB

Step3:

Display the image

Step4:

Set the pixels to display the ROI

Step5:

Perform bit wise conjunction of the two arrays using bitwise_and

Step6:

Display the segmented ROI from an image.

II)Perform handwritting detection in an image

Step1:

Import necessary packages

Step2:

Define a function to read the image,Convert the image to grayscale,Apply Gaussian blur to reduce noise and improve edge detection,Use Canny edge detector to find edges in the image,Find contours in the edged image,Filter contours based on area to keep only potential text regions,Draw bounding boxes around potential text regions.

Step3:

Display the results.

III)Perform object detection with label in an image

Step1:

Import necessary packages

Step2:

Set and add the config_file,weights to ur folder.

Step3:

Use a pretrained Dnn model (MobileNet-SSD v3)

Step4:

Create a classLabel and print the same

Step5:

Display the image using imshow()

Step6:

Set the model and Threshold to 0.5

Step7:

Flatten the index,confidence.

Step8:

Display the result.

PROGRAM:

I)Perform ROI from an image

import cv2
import numpy as np

image = cv2.imread('owl.jpg')
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
cv2.imshow('Original Image', image_rgb)
cv2.waitKey(0)
cv2.destroyAllWindows()
roi_mask = np.zeros_like(image_rgb)
roi_mask[100:300, 200:400, :] = 255  
segmented_roi = cv2.bitwise_and(image_rgb, roi_mask)
cv2.imshow('Segmented ROI', segmented_roi)
cv2.waitKey(0)
cv2.destroyAllWindows()

OUTPUT:

II)Perform handwritting detection in an image

import cv2
import numpy as np
import matplotlib.pyplot as plt

def detect_handwriting(image_path):
    img = cv2.imread(image_path)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    blurred = cv2.GaussianBlur(gray, (5, 5), 0)
    edges = cv2.Canny(blurred, 50, 150)
    contours, _ = cv2.findContours(edges.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    min_area = 100
    text_contours = [cnt for cnt in contours if cv2.contourArea(cnt) > min_area]
    img_copy = img.copy()
    for contour in text_contours:
        x, y, w, h = cv2.boundingRect(contour)
        cv2.rectangle(img_copy, (x, y), (x + w, y + h), (0, 255, 0), 2)
        
    img_rgb = cv2.cvtColor(img_copy, cv2.COLOR_BGR2RGB)
    plt.imshow(img_rgb)
    plt.title('Handwriting Detection')
    plt.axis('off')
    plt.show()
    
image_path = 'text.jpg'
detect_handwriting(image_path)

OUTPUT:

III)Perform object detection with label in an image

config_file='ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt'
frozen_model='frozen_inference_graph.pb'

model=cv2.dnn_DetectionModel(frozen_model,config_file)

classLabels = []
file_name='Labels.txt'
with open(file_name,'rt')as fpt:
    classLabels=fpt.read().rstrip('\n').split('\n')

print(classLabels)
print(len(classLabels))
img=cv2.imread('aeroplane.jpg')
plt.imshow(img)
plt.imshow(cv2.cvtColor(img,cv2.COLOR_BGR2RGB))
model.setInputSize(320,320)
model.setInputScale(1.0/127.5)#255/2=127.5
model.setInputMean((127.5,127.5,127.5))
model.setInputSwapRB(True)
ClassIndex,confidence,bbox=model.detect(img,confThreshold=0.5)
print(ClassIndex)
font_scale=3
font=cv2.FONT_HERSHEY_PLAIN
for ClassInd,conf,boxes in zip(ClassIndex.flatten(),confidence.flatten(),bbox):
    cv2.rectangle(img,boxes,(0,0,255),2)
    cv2.putText(img,classLabels[ClassInd-1],(boxes[0]+10,boxes[1]+40),font,fontScale=font_scale,color=(255,0,0),thickness=1)
plt.imshow(cv2.cvtColor(img,cv2.COLOR_BGR2RGB))

OUTPUT:

Result:

Thus, a python program using OpenCV for following image manipulations is done successfully.